Random forests and gradient boosting for wind energy prediction

Abstract

The ability of ensemble models to retain the bias of their learners while decreasing their individual variance has long made them quite attractive in a number of classification and regression problems. Moreover, when trees are used as learners, the relative simplicity of the resulting models has led to a renewed interest on them on Big Data problems. In this work we will study the application of Random Forest Regression (RFR) and Gradient Boosted Regression (GBR) to global and local wind energy prediction problems working with their high quality implementations in the Scikit-learn Python libraries. Besides a complete exploration of the RFR and GBR application to wind energy prediction, we will show experimentally that both ensemble methods can improve on SVR for individual wind farm energy prediction and that at least GBR is also competitive when the interest lies in predicting wind energy in a much larger geographical scale.